Automated screening of COVID-19 using two-dimensional variational mode decomposition and locally linear embedding

Liyuan Ma; Xipeng Xu; Changcai Cui; Jingyi Lu; Qifeng Hua; Hao Sun

doi:10.1016/j.bspc.2022.103889

Automated screening of COVID-19 using two-dimensional variational mode decomposition and locally linear embedding

Biomed Signal Process Control. 2022 Sep:78:103889. doi: 10.1016/j.bspc.2022.103889. Epub 2022 Jun 22.

Authors

Liyuan Ma^{1

2}, Xipeng Xu^{1

2}, Changcai Cui^{1

2}, Jingyi Lu^{3

4}, Qifeng Hua⁵, Hao Sun⁶

Affiliations

¹ National and Local Joint Engineering Research Center for Intelligent Manufacturing Technology of Brittle Material Products, Huaqiao University, Xiamen 361021, China.
² Institute of Manufacturing Engineering, Huaqiao University, Xiamen 361021, China.
³ School of Electrical and Information Engineering, Northeast Petroleum University, Daqing 163318, China.
⁴ Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Northeast Petroleum University, Daqing 163318, China.
⁵ Department of Radiology, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, 315010, China.
⁶ School of Mechanical Engineering, Fuzhou University, Fuzhou, 350108, China.

Abstract

In order to aid imaging physicians to effectively screen chest radiography medical images for presence of Coronavirus Disease 2019 (COVID-19), a novel computer aided diagnosis technology for automatic processing of COVID-19 images is proposed based on two-dimensional variational mode decomposition (2D-VMD) and locally linear embedding (LLE). 2D-VMD algorithm is used to decompose normal and COVID-19 images, and then feature extraction of intrinsic mode functions (IMFs) using Gabor filter. To better extract low-dimensional parameters which are useful for COVID-19 diagnosis, the performance of two dimensionality reduction techniques of principal component analysis (PCA) and LLE are compared, and the LLE is shown to offer satisfactory effect of dimension reduction. Thereafter, the particle swarm optimization-support vector machine (PSO-SVM) algorithm is used to classify. The simulation results show that the proposed technology has achieved accuracy of 99.33%, precision of 100%, recall of 98.63% and F-Measure of 99.31%. Hence, the developed diagnosis technology can be used as an important auxiliary tool to assist diagnosis of imaging physicians.

Keywords: Gabor filter; Locally linear embedding; Particle swarm optimization; Support vector machine; Two-dimensional variational mode decomposition.